Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	27.929018
daal4py_KNeighborsClassifier	0.0	3.0	28.180371
KNeighborsClassifier_kd_tree	0.0	6.0	25.288477
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	46.040990
KMeans_tall	0.0	1.0	40.003804
daal4py_KMeans_tall	0.0	1.0	16.258772
KMeans_short	0.0	0.0	17.791250
daal4py_KMeans_short	0.0	0.0	8.693973
LogisticRegression	0.0	1.0	7.899610
daal4py_LogisticRegression	0.0	0.0	31.369216
Ridge	0.0	0.0	25.323295
daal4py_Ridge	0.0	0.0	6.586265
total	0.0	30.0	41.440431

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	0.133	0.002	1000000	1000000	100	-1	1	6.029	NaN	0.984	0.977	0.502	0.008	0.264	0.006	See	See
1	KNeighborsClassifier	predict	25.129	0.130	1000000	1000	100	-1	1	0.000	0.025	0.984	0.977	2.030	0.040	12.381	0.253	See	See
2	KNeighborsClassifier	predict	0.163	0.017	1000000	1	100	-1	1	0.005	0.000	0.984	0.977	0.082	0.001	1.982	0.204	See	See
3	KNeighborsClassifier	fit	0.132	0.004	1000000	1000000	100	-1	5	6.043	NaN	0.984	0.977	0.477	0.004	0.277	0.009	See	See
4	KNeighborsClassifier	predict	33.448	0.000	1000000	1000	100	-1	5	0.000	0.033	0.984	0.977	2.044	0.052	16.365	0.418	See	See
5	KNeighborsClassifier	predict	0.169	0.016	1000000	1	100	-1	5	0.005	0.000	0.984	0.977	0.086	0.006	1.953	0.230	See	See
6	KNeighborsClassifier	fit	0.125	0.000	1000000	1000000	100	-1	100	6.424	NaN	0.984	0.977	0.478	0.002	0.260	0.001	See	See
7	KNeighborsClassifier	predict	33.407	0.000	1000000	1000	100	-1	100	0.000	0.033	0.984	0.977	2.091	0.031	15.974	0.238	See	See
8	KNeighborsClassifier	predict	0.165	0.018	1000000	1	100	-1	100	0.005	0.000	0.984	0.977	0.083	0.000	1.989	0.211	See	See
9	KNeighborsClassifier	fit	0.125	0.001	1000000	1000000	100	1	1	6.413	NaN	0.984	0.977	0.479	0.003	0.261	0.002	See	See
10	KNeighborsClassifier	predict	13.298	0.016	1000000	1000	100	1	1	0.000	0.013	0.984	0.977	2.017	0.021	6.595	0.068	See	See
11	KNeighborsClassifier	predict	0.181	0.004	1000000	1	100	1	1	0.004	0.000	0.984	0.977	0.083	0.001	2.186	0.045	See	See
12	KNeighborsClassifier	fit	0.125	0.000	1000000	1000000	100	1	5	6.389	NaN	0.984	0.977	0.478	0.004	0.262	0.002	See	See
13	KNeighborsClassifier	predict	22.153	0.035	1000000	1000	100	1	5	0.000	0.022	0.984	0.977	2.051	0.021	10.803	0.111	See	See
14	KNeighborsClassifier	predict	0.178	0.002	1000000	1	100	1	5	0.005	0.000	0.984	0.977	0.083	0.000	2.148	0.026	See	See
15	KNeighborsClassifier	fit	0.136	0.003	1000000	1000000	100	1	100	5.866	NaN	0.984	0.977	0.483	0.004	0.283	0.006	See	See
16	KNeighborsClassifier	predict	22.198	0.003	1000000	1000	100	1	100	0.000	0.022	0.984	0.977	2.091	0.018	10.618	0.092	See	See
17	KNeighborsClassifier	predict	0.176	0.002	1000000	1	100	1	100	0.005	0.000	0.984	0.977	0.085	0.001	2.080	0.041	See	See
18	KNeighborsClassifier	fit	0.061	0.000	1000000	1000000	2	-1	1	0.264	NaN	0.984	0.977	0.108	0.003	0.561	0.016	See	See
19	KNeighborsClassifier	predict	21.669	0.020	1000000	1000	2	-1	1	0.000	0.022	0.984	0.977	0.318	0.011	68.094	2.339	See	See
20	KNeighborsClassifier	predict	0.020	0.001	1000000	1	2	-1	1	0.001	0.000	0.984	0.977	0.007	0.001	3.012	0.473	See	See
21	KNeighborsClassifier	fit	0.061	0.000	1000000	1000000	2	-1	5	0.261	NaN	0.984	0.977	0.108	0.003	0.569	0.017	See	See
22	KNeighborsClassifier	predict	31.777	0.000	1000000	1000	2	-1	5	0.000	0.032	0.984	0.977	0.317	0.005	100.400	1.433	See	See
23	KNeighborsClassifier	predict	0.028	0.001	1000000	1	2	-1	5	0.001	0.000	0.984	0.977	0.006	0.001	4.492	0.776	See	See
24	KNeighborsClassifier	fit	0.061	0.001	1000000	1000000	2	-1	100	0.264	NaN	0.984	0.977	0.108	0.002	0.562	0.010	See	See
25	KNeighborsClassifier	predict	32.196	0.000	1000000	1000	2	-1	100	0.000	0.032	0.984	0.977	0.375	0.004	85.849	0.995	See	See
26	KNeighborsClassifier	predict	0.029	0.002	1000000	1	2	-1	100	0.001	0.000	0.984	0.977	0.008	0.000	3.789	0.321	See	See
27	KNeighborsClassifier	fit	0.064	0.002	1000000	1000000	2	1	1	0.251	NaN	0.984	0.977	0.110	0.003	0.583	0.025	See	See
28	KNeighborsClassifier	predict	10.397	0.216	1000000	1000	2	1	1	0.000	0.010	0.984	0.977	0.318	0.008	32.732	1.066	See	See
29	KNeighborsClassifier	predict	0.015	0.000	1000000	1	2	1	1	0.001	0.000	0.984	0.977	0.006	0.001	2.380	0.287	See	See
30	KNeighborsClassifier	fit	0.061	0.000	1000000	1000000	2	1	5	0.260	NaN	0.984	0.977	0.108	0.004	0.567	0.019	See	See
31	KNeighborsClassifier	predict	20.387	0.006	1000000	1000	2	1	5	0.000	0.020	0.984	0.977	0.318	0.005	64.108	1.099	See	See
32	KNeighborsClassifier	predict	0.023	0.001	1000000	1	2	1	5	0.001	0.000	0.984	0.977	0.006	0.000	3.645	0.293	See	See
33	KNeighborsClassifier	fit	0.060	0.000	1000000	1000000	2	1	100	0.266	NaN	0.984	0.977	0.107	0.002	0.564	0.011	See	See
34	KNeighborsClassifier	predict	20.495	0.040	1000000	1000	2	1	100	0.000	0.020	0.984	0.977	0.389	0.026	52.703	3.459	See	See
35	KNeighborsClassifier	predict	0.023	0.001	1000000	1	2	1	100	0.001	0.000	0.984	0.977	0.006	0.001	3.528	0.580	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	3.152	0.036	1000000	1000000	10	-1	1	0.025	NaN	0.983	0.99	0.751	0.022	4.196	0.131	See	See
1	KNeighborsClassifier_kd_tree	predict	0.476	0.014	1000000	1000	10	-1	1	0.000	0.000	0.983	0.99	0.106	0.007	4.482	0.326	See	See
2	KNeighborsClassifier_kd_tree	predict	0.005	0.002	1000000	1	10	-1	1	0.016	0.000	0.983	0.99	0.001	0.000	9.692	5.604	See	See
3	KNeighborsClassifier_kd_tree	fit	3.209	0.103	1000000	1000000	10	-1	5	0.025	NaN	0.983	0.99	0.767	0.014	4.185	0.155	See	See
4	KNeighborsClassifier_kd_tree	predict	0.800	0.014	1000000	1000	10	-1	5	0.000	0.001	0.983	0.99	0.189	0.006	4.223	0.154	See	See
5	KNeighborsClassifier_kd_tree	predict	0.005	0.006	1000000	1	10	-1	5	0.015	0.000	0.983	0.99	0.001	0.000	6.422	7.746	See	See
6	KNeighborsClassifier_kd_tree	fit	3.177	0.040	1000000	1000000	10	-1	100	0.025	NaN	0.983	0.99	0.732	0.011	4.337	0.085	See	See
7	KNeighborsClassifier_kd_tree	predict	2.762	0.052	1000000	1000	10	-1	100	0.000	0.003	0.983	0.99	0.571	0.014	4.841	0.150	See	See
8	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	-1	100	0.018	0.000	0.983	0.99	0.001	0.000	3.195	1.160	See	See
9	KNeighborsClassifier_kd_tree	fit	3.126	0.076	1000000	1000000	10	1	1	0.026	NaN	0.983	0.99	0.751	0.007	4.165	0.107	See	See
10	KNeighborsClassifier_kd_tree	predict	0.722	0.004	1000000	1000	10	1	1	0.000	0.001	0.983	0.99	0.102	0.001	7.076	0.105	See	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	1	1	0.099	0.000	0.983	0.99	0.001	0.000	1.621	0.654	See	See
12	KNeighborsClassifier_kd_tree	fit	3.035	0.017	1000000	1000000	10	1	5	0.026	NaN	0.983	0.99	0.723	0.008	4.196	0.051	See	See
13	KNeighborsClassifier_kd_tree	predict	1.428	0.017	1000000	1000	10	1	5	0.000	0.001	0.983	0.99	0.192	0.006	7.448	0.235	See	See
14	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	1	5	0.067	0.000	0.983	0.99	0.001	0.000	1.497	0.668	See	See
15	KNeighborsClassifier_kd_tree	fit	3.048	0.018	1000000	1000000	10	1	100	0.026	NaN	0.983	0.99	0.756	0.019	4.029	0.101	See	See
16	KNeighborsClassifier_kd_tree	predict	4.598	0.016	1000000	1000	10	1	100	0.000	0.005	0.983	0.99	0.591	0.020	7.785	0.265	See	See
17	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	1	100	0.033	0.000	0.983	0.99	0.001	0.000	1.746	0.719	See	See
18	KNeighborsClassifier_kd_tree	fit	0.835	0.004	1000000	1000000	2	-1	1	0.019	NaN	0.983	0.99	0.497	0.005	1.678	0.019	See	See
19	KNeighborsClassifier_kd_tree	predict	0.029	0.003	1000000	1000	2	-1	1	0.001	0.000	0.983	0.99	0.001	0.000	37.015	10.633	See	See
20	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	-1	1	0.006	0.000	0.983	0.99	0.000	0.000	22.219	14.887	See	See
21	KNeighborsClassifier_kd_tree	fit	0.850	0.020	1000000	1000000	2	-1	5	0.019	NaN	0.983	0.99	0.492	0.002	1.728	0.042	See	See
22	KNeighborsClassifier_kd_tree	predict	0.031	0.001	1000000	1000	2	-1	5	0.001	0.000	0.983	0.99	0.001	0.000	21.860	7.386	See	See
23	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	-1	5	0.006	0.000	0.983	0.99	0.000	0.000	21.245	15.002	See	See
24	KNeighborsClassifier_kd_tree	fit	0.836	0.010	1000000	1000000	2	-1	100	0.019	NaN	0.983	0.99	0.501	0.011	1.671	0.042	See	See
25	KNeighborsClassifier_kd_tree	predict	0.051	0.002	1000000	1000	2	-1	100	0.000	0.000	0.983	0.99	0.007	0.001	7.069	1.010	See	See
26	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	-1	100	0.006	0.000	0.983	0.99	0.000	0.000	20.146	12.730	See	See
27	KNeighborsClassifier_kd_tree	fit	0.834	0.005	1000000	1000000	2	1	1	0.019	NaN	0.983	0.99	0.505	0.009	1.654	0.032	See	See
28	KNeighborsClassifier_kd_tree	predict	0.026	0.001	1000000	1000	2	1	1	0.001	0.000	0.983	0.99	0.001	0.000	29.427	8.344	See	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	1	0.023	0.000	0.983	0.99	0.000	0.000	5.965	4.278	See	See
30	KNeighborsClassifier_kd_tree	fit	0.838	0.004	1000000	1000000	2	1	5	0.019	NaN	0.983	0.99	0.517	0.033	1.622	0.102	See	See
31	KNeighborsClassifier_kd_tree	predict	0.028	0.001	1000000	1000	2	1	5	0.001	0.000	0.983	0.99	0.002	0.000	17.990	5.609	See	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	5	0.024	0.000	0.983	0.99	0.000	0.000	5.724	4.130	See	See
33	KNeighborsClassifier_kd_tree	fit	0.840	0.003	1000000	1000000	2	1	100	0.019	NaN	0.983	0.99	0.502	0.013	1.674	0.043	See	See
34	KNeighborsClassifier_kd_tree	predict	0.055	0.001	1000000	1000	2	1	100	0.000	0.000	0.983	0.99	0.007	0.001	7.382	1.115	See	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	1	100	0.022	0.000	0.983	0.99	0.000	0.000	5.406	3.594	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	init	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	0.617	0.013	1000000	1000000	2	k-means++	30	0.777	NaN	0.002	30	0.002	0.502	0.036	1.229	0.092	See	See
1	KMeans_tall	predict	0.001	0.000	1000000	1000	2	k-means++	30	0.011	0.0	0.002	30	0.002	0.000	0.000	9.072	5.148	See	See
2	KMeans_tall	predict	0.001	0.000	1000000	1	2	k-means++	30	0.011	0.0	0.002	30	0.002	0.000	0.000	11.219	7.022	See	See
3	KMeans_tall	fit	0.564	0.006	1000000	1000000	2	random	30	0.851	NaN	0.002	30	0.002	0.461	0.041	1.225	0.111	See	See
4	KMeans_tall	predict	0.002	0.000	1000000	1000	2	random	30	0.011	0.0	0.002	30	0.002	0.000	0.000	8.219	4.358	See	See
5	KMeans_tall	predict	0.001	0.000	1000000	1	2	random	30	0.011	0.0	0.002	30	0.002	0.000	0.000	10.556	5.999	See	See
6	KMeans_tall	fit	6.453	0.119	1000000	1000000	100	k-means++	30	3.719	NaN	0.002	30	0.002	2.987	0.040	2.161	0.049	See	See
7	KMeans_tall	predict	0.002	0.001	1000000	1000	100	k-means++	30	0.371	0.0	0.002	30	0.002	0.000	0.000	7.645	3.539	See	See
8	KMeans_tall	predict	0.001	0.000	1000000	1	100	k-means++	30	0.558	0.0	0.002	30	0.002	0.000	0.000	10.299	5.823	See	See
9	KMeans_tall	fit	6.041	0.099	1000000	1000000	100	random	30	3.973	NaN	0.002	30	0.002	2.774	0.020	2.177	0.039	See	See
10	KMeans_tall	predict	0.002	0.000	1000000	1000	100	random	30	0.439	0.0	0.002	30	0.002	0.000	0.000	6.436	2.401	See	See
11	KMeans_tall	predict	0.001	0.000	1000000	1	100	random	30	0.557	0.0	0.002	30	0.002	0.000	0.000	9.781	5.257	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	init	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	0.263	0.003	10000	10000	2	k-means++	20	0.012	NaN	0.262	20	0.298	0.102	0.002	2.579	0.068	See	See
1	KMeans_short	predict	0.002	0.001	10000	1000	2	k-means++	20	0.007	0.0	0.262	20	0.298	0.001	0.000	3.005	1.576	See	See
2	KMeans_short	predict	0.001	0.000	10000	1	2	k-means++	20	0.011	0.0	0.262	20	0.298	0.000	0.000	9.669	5.227	See	See
3	KMeans_short	fit	0.090	0.004	10000	10000	2	random	20	0.036	NaN	0.262	20	0.298	0.037	0.005	2.454	0.351	See	See
4	KMeans_short	predict	0.002	0.000	10000	1000	2	random	20	0.008	0.0	0.262	20	0.298	0.001	0.000	2.647	0.603	See	See
5	KMeans_short	predict	0.001	0.000	10000	1	2	random	20	0.011	0.0	0.262	20	0.298	0.000	0.000	9.426	6.779	See	See
6	KMeans_short	fit	0.678	0.011	10000	10000	100	k-means++	20	0.236	NaN	0.262	20	0.298	0.369	0.009	1.835	0.052	See	See
7	KMeans_short	predict	0.003	0.000	10000	1000	100	k-means++	20	0.278	0.0	0.262	20	0.298	0.001	0.000	1.969	0.382	See	See
8	KMeans_short	predict	0.002	0.000	10000	1	100	k-means++	20	0.511	0.0	0.262	20	0.298	0.000	0.000	8.618	4.057	See	See
9	KMeans_short	fit	0.233	0.009	10000	10000	100	random	20	0.685	NaN	0.262	20	0.298	0.149	0.003	1.566	0.068	See	See
10	KMeans_short	predict	0.003	0.000	10000	1000	100	random	20	0.283	0.0	0.262	20	0.298	0.001	0.000	2.030	0.396	See	See
11	KMeans_short	predict	0.002	0.000	10000	1	100	random	20	0.513	0.0	0.262	20	0.298	0.000	0.000	8.284	4.469	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	class_weight	l1_ratio	n_jobs	random_state	n_iter	throughput	latency	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	12.374	0.301	1000000	1000000	100	NaN	NaN	NaN	NaN	[20]	[-0.09535207]	NaN	0.25	1.945	0.026	6.363	0.176	See	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	NaN	NaN	NaN	NaN	[20]	2.5134737900215343	0.0	0.25	0.000	0.000	0.813	0.342	See	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	NaN	NaN	NaN	NaN	[20]	10.878270785963549	0.0	0.25	0.000	0.000	0.374	0.308	See	See
3	LogisticRegression	fit	0.793	0.008	1000	1000	10000	NaN	NaN	NaN	NaN	[28]	[-2.5911627]	NaN	0.25	0.721	0.031	1.100	0.049	See	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	NaN	NaN	NaN	NaN	[28]	4.571599157145027	0.0	0.25	0.003	0.000	0.545	0.090	See	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	NaN	NaN	NaN	NaN	[28]	73.30599020375392	0.0	0.25	0.001	0.000	0.144	0.079	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	max_iter	random_state	throughput	latency	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	0.187	0.002	1000	1000	10000	NaN	NaN	0.428	NaN	1.0	0.173	0.002	1.083	0.014	See	See
1	Ridge	predict	0.010	0.000	1000	1000	10000	NaN	NaN	8.072	0.0	1.0	0.017	0.000	0.588	0.018	See	See
2	Ridge	predict	0.000	0.000	1000	1	10000	NaN	NaN	969.565	0.0	1.0	0.000	0.000	0.662	0.614	See	See
3	Ridge	fit	1.168	0.049	1000000	1000000	100	NaN	NaN	0.685	NaN	1.0	0.243	0.006	4.815	0.233	See	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	NaN	NaN	4.415	0.0	1.0	0.000	0.000	0.709	0.386	See	See
5	Ridge	predict	0.000	0.000	1000000	1	100	NaN	NaN	12.019	0.0	1.0	0.000	0.000	0.628	0.566	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}